# Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

arXiv: https://arxiv.org/abs/2407.02536 · HTML: https://mlsanity.com/paper/2407.02536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Towards a Tighter Bound on Possible-Rendezvous Areas: Preliminary Results](https://mlsanity.com/paper/2206.12756/) (2022)

## Built on by (most cited first, 3 of 3)

- [Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling](https://mlsanity.com/paper/2410.15628/) (2024)
- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [Towards Physics-Guided Foundation Models](https://mlsanity.com/paper/2502.15013/) (2025)

## Abstract (arXiv)

Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs <a region (rg), a subset C of S> such that C is a statistically significant regional-colocation pattern in rg. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner cite10.1145/3557989.3566158 that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost.